The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
Sequence-discriminative training of deep neural networks
Open accessAbstract
Sequence-discriminative training of deep neural networks (DNNs) is investigated on a 300 hour American English conversational telephone speech task. Different sequence-discriminative criteria ndash;- maximum mutual information (MMI), minimum phone error (MPE), state-level minimum Bayes risk (sMBR), and boosted MMI ndash;- are compared. Two different heuristics are investigated to improve the performance of the DNNs trained using sequence-based criteria ndash;- lattices are re-generated after the first iteration of training; and, for MMI and BMMI, the frames where the numerator and denominator hypotheses are disjoint are removed from the gradient computation. Starting from a competitive DNN baseline trained using cross-entropy, different sequence-discriminative criteria are shown to lower word error rates by 8-9% relative, on average. Little difference is noticed between the different sequence-based criteria that are investigated. The experiments are done using the open-source Kaldi toolkit, which makes it possible for the wider community to reproduce these results.
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Cites
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Self-citationLarge Scale Distributed Deep Networks
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Cited by
SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition
Park, D. S., Chan, W., Zhang, Y., Chiu, C.-C., Zoph, B., Cubuk, E. D., & Le, Q. V. (2019) · Interspeech 2019 · doi:10.21437/interspeech.2019-2680 · Cited by 3,558
Deep Learning: Methods and Applications
Deng, L., & Yu, D. (2014) · Foundations and Trends® in Signal Processing · doi:10.1561/2000000039 · Cited by 3,480
Listen, attend and spell: A neural network for large vocabulary conversational speech recognition
Chan, W., Jaitly, N., Le, Q., & Vinyals, O. (2016) · 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2016.7472621 · Cited by 2,226
Deep Learning: Methods and Applications
Deng, L. (2014) · now publishers, Inc. eBooks · doi:10.1561/9781601988157 · Cited by 1,759
A time delay neural network architecture for efficient modeling of long temporal contexts
Peddinti, V., Povey, D., & Khudanpur, S. (2015) · Interspeech 2015 · doi:10.21437/interspeech.2015-647 · Cited by 986
Self-citation
Metadata
- Authors
- Veselý, K., Ghoshal, A., Burget, L., & Povey, D.
- Venue
- Interspeech 2013
- Publisher
- ISCA
- Published
- 2013-08-25
- Pages
- 2345-2349
- ISSN
- 2308-457X (ISSN-L)
- OpenAlex
- W2131342762
- Cited by
- 657
- References
- 22
- FWCI
- 56.81
- Type
- conference paper
- Language
- en
- Open access
- green
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